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Xiongxin Yang

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Articles

Computer Science > Discrete Mathematics

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By Eric Vigoda, Xiongxin Yang
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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Computer Science > Machine Learning

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By Xiongxin Yang, Yixiao Yu, Yiyao Zhang
| arXiv
The problem of learning constant-depth circuits holds profound implications for computational learning theory. In a seminal result, by introducing the low-degree algorithm, Linial, Mansour, and Nisan (J. ACM 1993) presented a quasipolynomial-time learner for $\mathsf{AC}^0$ under the uniform distribution. However, obtaining comparable learning guarantees for broader classes of correlated distributions has remained a longstanding challenge.
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[2511.02487] Learning CNF formulas from uniform random solutions in the local lemma regime

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By Xiongxin Yang, Yixiao Yu, Yiyao Zhang
| arXiv
arXiv:2511.02487 (cs) View PDF HTML (experimental) Subjects: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2511.02487 [cs.DS] (or arXiv:2511.02487v1 [cs.DS] for this version) https://doi.org/10.48550/arXiv.2511.02487 Submission history From: Yiyao Zhang [ view email] [v1] Tue, 4 Nov 2025 11:22:05 UTC (158 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Connected Papers Toggle Connected Papers (What...
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